This article describes a framework for maximum entropy dasymetric modeling based on spatial allocations of public use microdata sample (PUMS) files provided by the U.S. Census Bureau. The spatial units of the PUMS (PUMAs; public use microdata areas) are too large for fine‐scale geographic analysis of populations because the common expectation is high degrees of variation within one PUMA (containing about 100,000 people). Limited demographic attribution is available at finer spatial resolutions in census summary tables for tracts and block groups. The described method (i.e., the coupling of spatial allocation procedures with dasymetric modeling) extends the literature and implements related variable associations and limiting variable constraints for allocating microdata household records to census tracts, based on sampling weights imputed using maximum entropy models. We present techniques to quantify household‐level uncertainty and to show how this information is useful for guiding the dasymetric modeling and for improving the choice of limiting and related ancillary variables. We demonstrate our methods with a PUMA in Davidson County, Tennessee. Census summary statistics are used as related variables, and land cover‐derived residential areas are included as limiting variables to refine the solution spatially to a subtract level. 本文提出了基于美国人口统计局提供的公众微观采样数据（PUMS）的空间布局分析的最大熵密度建模框架。因一个PUMA(包含10万人的统计区)通常被认为有高的变异度，导致其空间单元对于精细尺度的人口地理分析而言过大。在区块组的人口普查表中少有精细尺度的人口属性收。本文所提方法（即基于密度建模的耦合空间配置过程），基于最大熵模型对采样权重进行填充，扩展了现有方法，实现了相关变量的集成以及面向微观住户记录对空间配置的限制变量约束。我们给出了量化住户水平的不确定性量化技术，展示了该信息如何有效指导了密度建模以及增强了限制变量和相关辅助变量的选择。以西安纳西州戴维森的一个PUMA的基础统计作为相关变量，并将土地覆盖‐居民区分布作为限制变量，演示了将结果空间精细化至向亚调查块层次。